Optimized XGBoost-Based Model for Accurate Detection and Classification of COVID-19 Pneumonia

Abstract

The accurate diagnosis of COVID-19 pneumonia is a critical global health challenge, particularly for vulnerable populations. Existing diagnostic methods often lack precision due to limited algorithm sophistication and insufficient dataset validation. This study addresses these issues by introducing a customized XGBoost algorithm for classifying COVID-19 pneumonia. The methodology follows a four-phase approach: (1) data acquisition from a comprehensive GitHub dataset, (2) data preprocessing with augmentation and normalization, (3) model training using XGBoost, and (4) evaluation against existing models. The model achieves an average accuracy of 87.35%, demonstrating superior performance in accuracy and diagnostic precision compared to current methods. The findings of this research provides a systematic framework for improving pneumonia classification and sets the stage for future AI-driven healthcare advancements in respiratory diseases.